How and where it’s used in multiple ways. The moreīeautiful your data is here, the better it can be trusted and relied upon for Maintenance, better assessment of risks and issues, and more. Streamlining processes, greater effectiveness with preventative asset Of uses, whether that be improved customer engagement, efficiency in In situ applications is generally the improvement of the data for a multitude The purpose for data quality management for These areĮxisting applications embedded in your organisation that are used day to day byĪ variety of staff carrying out their duties creating and curating data. Management, or any other applications used to run a business. When I use the term “applications,” thinkĬustomer Relationship Management, Asset Management, Enterprise Resource Is, you have your existing applications used for day to day purposes for theĬapture and curation of data aligned to business processes and outcomes. 1Īpplication of data quality management to existing in-use applications.
Butterfly 1: In Situ Data Quality Managementįirst, let’s start with a definition of In Situ to set the context.ĭefinition of in situ: in the natural or original position or place. Terms for this article I’ll refer to them as Butterfly 1 and Butterfly 2 My perspectives, and that’s great, or you’ll challenge my perspective and weĬomparison, first we must apply understanding to In Situ and Data Migration In the end, I hope you’ll either agree with Of this is new, just that I’ve not seen this comparison before.
My observations and reading of content shared by many well-educated peers. This is based on my years of applicable experience in both domains and Quality management techniques and scopes are applied, which I’ll attempt toĮxplain. There are similarities and differences, though, in how the data The trick is determining how beautiful you want your data, a subjective assessment.Ībout the application of quality management techniques to the enterprise asset Why butterfly? Because they are beautiful, delicate yet resilient creatures, great in number and variety. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use in data migration. We know the phrase, “Beauty is in the eye of the beholder.” 1 In this article, I will apply it to the topic of data quality.
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